DESCRIPTION (Applicant's abstract): The long-term objective of the proposed research is to better understand the nature of the perceptual and cognitive processes involved when a person identifies or categorizes objects. Two lines of research are outlined. The first examines the optimality of human categorization performance when base-rates and payoffs are manipulated simultaneously. Experiments are outlined that examine the effect of (a) stimulus properties, (b) category discriminability, and (c) the costs and benefits associated with various categorization responses on base-rate and payoff sensitivity. All experiment will use the perceptual categorization task (e.g., Ashby & Cott, 1988; Maddox, 1995) in which two normally distributed categories are specified and a large number of category exemplars are sampled from each category distribution. The use of normally distributed categories allows the optimal decision rule to be derived. The approach is to apply a series of quantitative models to the trial-by-trial learning data and to measures of asymptotic performance. Each model will embody specific hypotheses about the optimality, or potential sub-optimality, of responding. Standard categorization procedures as well as hypothetical medical diagnosis procedures will be utilized. These studies will inform many real-world categorization problems, such as medical diagnosis, where base-rates and payoffs often differ and vary simultaneously. The second line of research examines perceptual and decisional attention processes in identification and categorization. Experiments are outlined that examine the effects of (a) attention cue and decision rule manipulations, as well as (b) stimulus property and response deadline manipulations on perceptual and decisional attention processes. To better understand the nature of these attention systems, attempts will be made to account simultaneously for accuracy and response time (RT) data within a single theoretical framework (e.g., Maddox & Ashby, 1996). To ensure stable estimates of the accuracy and RT data, each task will utilize a small number of unique stimuli (15-30) with 200-300 presentations of each stimulus in each condition. The approach is to apply a series of quantitative models that each embody specific hypotheses about the nature of, and interaction between, perceptual and decisional attention systems, and to compare these models with models derived from theories that do not distinguish between perceptual and decisional forms of attention (e.g., many exemplar-based models; Nosofsky, 1986). These studies are important because they will provide useful information about attention processes that are fundamental to nearly all types of human behavior.